Pınar Demetçi

SCOT+ aligns single-cell multi-omics data across datasets using optimal transport without requiring correspondence, improving downstream analysis accuracy.

This study identifies molecular signatures of ancestral cell types in mammalian olfactory cortex neurons by comparing transcriptomes across species and cortical layers.

This paper introduces a co-optimal transport method for aligning cells and genomic features in unpaired single-cell multi-omic datasets, improving integration with sparse or dense feature correspondences.

SCOT uses Gromov-Wasserstein optimal transport to align single-cell multi-omics data without requiring correspondence information for hyperparameter tuning, performing on par with state-of-the-art methods but faster.

The paper improves SCOT for integrating single-cell multi-omics datasets with imbalanced cell-type representation and varying sample sizes, showing better alignment performance on real-world datasets.

This paper introduces BANNs, a probabilistic framework for GWA studies that improves association mapping and enrichment analysis by incorporating biological annotations and variational inference.

This paper applies and extends MMD-MA to align single-cell transcriptome and chromatin accessibility data from ~2000 cells, demonstrating its scalability and accuracy.